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log user name during chat

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binary-husky
2024-11-10 16:50:24 +00:00
parent 197287fc30
commit 82e125d439
12 changed files with 19 additions and 17 deletions
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2
.gitignore vendored
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@@ -161,3 +161,5 @@ temp.*
objdump*
*.min.*.js
TODO
experimental_mods
search_results

4
request_llms/bridge_chatgpt.py
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@@ -341,7 +341,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
# 前者是API2D的结束条件，后者是OPENAI的结束条件
if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
# 判定为数据流的结束，gpt_replying_buffer也写完了
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
break
# 处理数据流的主体
status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -375,7 +375,7 @@ def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
try:
chunkjson = json.loads(response.content.decode())
gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

2
request_llms/bridge_chatgpt_vision.py
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@@ -184,7 +184,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
# 判定为数据流的结束，gpt_replying_buffer也写完了
lastmsg = chatbot[-1][-1] + f"\n\n\n\n「{llm_kwargs['llm_model']}调用结束，该模型不具备上下文对话能力，如需追问，请及时切换模型。」"
yield from update_ui_lastest_msg(lastmsg, chatbot, history, delay=1)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
break
# 处理数据流的主体
status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"

2
request_llms/bridge_claude.py
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@@ -216,7 +216,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
if need_to_pass:
pass
elif is_last_chunk:
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
# logger.info(f'[response] {gpt_replying_buffer}')
break
else:

2
request_llms/bridge_cohere.py
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@@ -223,7 +223,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
if chunkjson['event_type'] == 'stream-end':
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面

2
request_llms/bridge_google_gemini.py
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@@ -109,7 +109,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
gpt_replying_buffer += paraphrase['text'] # 使用 json 解析库进行处理
chatbot[-1] = (inputs, gpt_replying_buffer)
history[-1] = gpt_replying_buffer
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
yield from update_ui(chatbot=chatbot, history=history)
if error_match:
history = history[-2] # 错误的不纳入对话

2
request_llms/bridge_moonshot.py
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@@ -166,7 +166,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
history = history[:-2]
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
break
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result, user_name=chatbot.get_user())
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
console_slience=False):

4
request_llms/bridge_openrouter.py
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@@ -337,7 +337,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
# 前者是API2D的结束条件，后者是OPENAI的结束条件
if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
# 判定为数据流的结束，gpt_replying_buffer也写完了
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
break
# 处理数据流的主体
status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -371,7 +371,7 @@ def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
try:
chunkjson = json.loads(response.content.decode())
gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

2
request_llms/bridge_qwen.py
View File

@@ -59,7 +59,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response, user_name=chatbot.get_user())
# 总结输出
if response == f"[Local Message] 等待{model_name}响应中 ...":
response = f"[Local Message] {model_name}响应异常 ..."

2
request_llms/bridge_taichu.py
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@@ -68,5 +68,5 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
chatbot[-1] = [inputs, response]
yield from update_ui(chatbot=chatbot, history=history)
history.extend([inputs, response])
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response, user_name=chatbot.get_user())
yield from update_ui(chatbot=chatbot, history=history)

2
request_llms/bridge_zhipu.py
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@@ -97,5 +97,5 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
chatbot[-1] = [inputs, response]
yield from update_ui(chatbot=chatbot, history=history)
history.extend([inputs, response])
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response, user_name=chatbot.get_user())
yield from update_ui(chatbot=chatbot, history=history)

8
toolbox.py
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@@ -1029,7 +1029,7 @@ def check_repeat_upload(new_pdf_path, pdf_hash):
# 如果所有页的内容都相同，返回 True
return False, None
def log_chat(llm_model: str, input_str: str, output_str: str):
def log_chat(llm_model: str, input_str: str, output_str: str, user_name: str=default_user_name):
try:
if output_str and input_str and llm_model:
uid = str(uuid.uuid4().hex)
@@ -1038,8 +1038,8 @@ def log_chat(llm_model: str, input_str: str, output_str: str):
logger.bind(chat_msg=True).info(dedent(
"""
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
[UID]
{uid}
[UID/USER]
{uid}/{user_name}
[Model]
{llm_model}
[Query]
@@ -1047,6 +1047,6 @@ def log_chat(llm_model: str, input_str: str, output_str: str):
[Response]
{output_str}
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
""").format(uid=uid, llm_model=llm_model, input_str=input_str, output_str=output_str))
""").format(uid=uid, user_name=user_name, llm_model=llm_model, input_str=input_str, output_str=output_str))
except:
logger.error(trimmed_format_exc())